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Found 6 papers, 1 papers with code
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization.
Testing Causality for High Dimensional Data
Determining causal relationship between high dimensional observations are among the most important tasks in scientific discoveries.
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
Probabilistic Forecasting: A Level-Set Approach
By recognizing the connection of our algorithm to random forests (RFs) and quantile regression forests (QRFs), we are able to prove consistency guarantees of our approach under mild assumptions on the underlying point estimator.
Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR
When the loss functions are strongly convex or exp-concave, we demonstrate that Strongly Adaptive (SA) algorithms can be viewed as a principled way of controlling dynamic regret in terms of path variation $V_T$ of the comparator sequence.
Normalizing Kalman Filters for Multivariate Time Series Analysis
This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting.
